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Multilevel modeling (MLM) is commonly used in psychological research to model clustered data. However, data in applied research usually violate one of the essential assumptions of MLM-homogeneity of variance. While the fixed-effect estimates produced by the maximum likelihood method remain unbiased, the standard errors for the fixed effects are misestimated, resulting in inaccurate inferences and inflated or deflated type I error rates. To correct the bias in fixed effects standard errors and provide valid inferences, small-sample corrections such as the Kenward-Roger (KR) adjustment and the adjusted cluster-robust standard errors (CR-SEs) with the Satterthwaite approximation for t tests have been used. The current study compares KR with random slope (RS) models and the adjusted CR-SEs with ordinary least squares (OLS), random intercept (RI) and RS models to analyze small, heteroscedastic, clustered data using a Monte Carlo simulation. Results show the KR procedure with RS models has large biases and inflated type I error rates for between-cluster effects in the presence of level 2 heteroscedasticity. In contrast, the adjusted CR-SEs generally yield results with acceptable biases and maintain type I error rates close to the nominal level for all examined models. Thus, when the interest is only in within-cluster effect, any model with the adjusted CR-SEs could be used. However, when the interest is to make accurate inferences of the between-cluster effect, researchers should use the adjusted CR-SEs with RS to have higher power and guard against unmodeled heterogeneity. We reanalyzed an example in Snijders & Bosker (2012) to demonstrate the use of the adjusted CR-SEs with different models., (© 2024. The Author(s).)

Most theories of suicide propose within-person changes in psychological states cause suicidal thoughts/behaviors; however, most studies use between-person analyses. Thus, there are little empirical data exploring current theories in the way they are hypothesized to occur. We used a form of statistical modeling called group iterative multiple model estimation (GIMME) to explore one theory of suicide: The Interpersonal Theory of Suicide (IPTS). GIMME estimates personalized statistical models for each individual and associations shared across individuals. Data were from a real-time monitoring study of individuals with a history of suicidal thoughts/behavior (adult sample: participants = 111, observations = 25,242; adolescent sample: participants = 145, observations = 26,182). Across both samples, none of theorized IPTS effects (i.e., contemporaneous effect from hopeless to suicidal thinking) were shared at the group level. There was significant heterogeneity in the personalized models, suggesting there are different pathways through which different people come to experience suicidal thoughts/behaviors. These findings highlight the complexity of suicide risk and the need for more personalized approaches to assessment and prediction., Competing Interests: Declaration of competing interest Dr. Nock receives publication royalties from Macmillan, Pearson, and UpToDate. He has been a paid consultant in the past three years for Microsoft Corporation, the Veterans Health Administration, and COMPASS Pathways, and for legal cases regarding a death by suicide. He has stock options in Cerebral Inc. He is an unpaid scientific advisor for Empatica, Koko, and TalkLife. Dr. Kleiman has been a paid consultant in the past three years for Boehringer Ingelheim Pharmaceuticals. Dr. Smoller is a member of the Scientific Advisory Board of Sensorium Therapeutics (with equity), and has received grant support from Biogen, Inc. He is PI of a collaborative study of the genetics of depression and bipolar disorder sponsored by 23andMe for which 23andMe provides analysis time as in-kind support but no payments. Dr. Onnela is a cofounder and board member of a commercial entity that operates in digital phenotyping., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Despite a growing body of literature in the area of recruitment modeling for multicenter studies, in practice, statistical models to predict enrollments are rarely used and when they are, they often rely on unrealistic assumptions. The time-dependent Poisson-Gamma model (tPG) is a recently developed flexible methodology which allows analysts to predict recruitments in an ongoing multicenter trial, and its performance has been validated on data from a cohort study. In this article, we illustrate and further validate the tPG model on recruitment data from randomized controlled trials. Additionally, in the appendix, we provide a practical and easy to follow guide to its implementation via the tPG R package. To validate the model, we show the predictive performance of the proposed methodology in forecasting the recruitment process of two HIV vaccine trials conducted by the HIV Vaccine Trials Network in multiple Sub-Saharan countries., Competing Interests: Declaration of competing interest None., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

The increased availability of large clinical datasets together with increasingly sophisticated computing power has facilitated development of numerous risk prediction models for various adverse perioperative outcomes, including acute kidney injury (AKI). The rationale for developing such models is straightforward. However, despite numerous purported benefits, the uptake of preoperative prediction models into clinical practice has been limited. Barriers to implementation of predictive models, including limitations in their discrimination and accuracy, as well as their ability to meaningfully impact clinical practice and patient outcomes, are increasingly recognised. Some of the purported benefits of predictive modelling, particularly when applied to postoperative AKI, might not fare well under detailed scrutiny. Future research should address existing limitations and seek to demonstrate both benefit to patients and value to healthcare systems from implementation of these models in clinical practice., (Copyright © 2024 British Journal of Anaesthesia. Published by Elsevier Ltd. All rights reserved.)

Medical image segmentation demands precise accuracy and the capability to assess segmentation uncertainty for informed clinical decision-making. Denoising Diffusion Probability Models (DDPMs), with their advancements in image generation, can treat segmentation as a conditional generation task, providing accurate segmentation and uncertainty estimation. However, current DDPMs used in medical image segmentation suffer from low inference efficiency and prediction errors caused by excessive noise at the end of the forward process. To address this issue, we propose an accelerated denoising diffusion probabilistic model via truncated inverse processes (ADDPM) that is specifically designed for medical image segmentation. The inverse process of ADDPM starts from a non-Gaussian distribution and terminates early once a prediction with relatively low noise is obtained after multiple iterations of denoising. We employ a separate powerful segmentation network to obtain pre-segmentation and construct the non-Gaussian distribution of the segmentation based on the forward diffusion rule. By further adopting a separate denoising network, the final segmentation can be obtained with just one denoising step from the predictions with low noise. ADDPM greatly reduces the number of denoising steps to approximately one-tenth of that in vanilla DDPMs. Our experiments on four segmentation tasks demonstrate that ADDPM outperforms both vanilla DDPMs and existing representative accelerating DDPMs methods. Moreover, ADDPM can be easily integrated with existing advanced segmentation models to improve segmentation performance and provide uncertainty estimation. Implementation code: https://github.com/Guoxt/ADDPM., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background and Objective: This paper proposes a fully automated and unsupervised stochastic segmentation approach using two-level joint Markov-Gibbs Random Field (MGRF) to detect the vascular system from retinal Optical Coherence Tomography Angiography (OCTA) images, which is a critical step in developing Computer-Aided Diagnosis (CAD) systems for detecting retinal diseases., Methods: Using a new probabilistic model based on a Linear Combination of Discrete Gaussian (LCDG), the first level models the appearance of OCTA images and their spatially smoothed images. The parameters of the LCDG model are estimated using a modified Expectation Maximization (EM) algorithm. The second level models the maps of OCTA images, including the vascular system and other retina tissues, using MGRF with analytically estimated parameters from the input images. The proposed segmentation approach employs modified self-organizing maps as a MAP-based optimizer maximizing the joint likelihood and handles the Joint MGRF model in a new, unsupervised way. This approach deviates from traditional stochastic optimization approaches and leverages non-linear optimization to achieve more accurate segmentation results., Results: The proposed segmentation framework is evaluated quantitatively on a dataset of 204 subjects. Achieving 0.92 ± 0.03 Dice similarity coefficient, 0.69 ± 0.25 95-percentile bidirectional Hausdorff distance, and 0.93 ± 0.03 accuracy, confirms the superior performance of the proposed approach., Conclusions: The conclusions drawn from the study highlight the superior performance of the proposed unsupervised and fully automated segmentation approach in detecting the vascular system from OCTA images. This approach not only deviates from traditional methods but also achieves more accurate segmentation results, demonstrating its potential in aiding the development of CAD systems for detecting retinal diseases., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Item response theory (IRT) analyses are often used to evaluate measurement error in educational and psychological test instruments. In such contexts, the latent traits/proficiencies are typically assumed normally distributed and a cumulative normal/logistic measurement link function is applied. Such choices are consistent with constructs that are viewed as bipolar in nature and play a critical role in defining the latent proficiency metric against which the measurement error in the test is evaluated. Recently, alternative models that portray the construct as unipolar have been highlighted as being more appropriate for certain psychopathology and personality constructs. In this paper we extend consideration of unipolar IRT models for a recognition task measure, using several example datasets from various versions of the Author Recognition Test (ART), a measure of print exposure. We show how the decision between unipolar versus bipolar IRT modeling has substantial implications for the quantification and interpretation of measurement error in the ART. In sharp contrast to prior bipolar IRT analyses of the ART, under unipolar IRT measurement error in the ART is minimized at low levels of latent print exposure, and increases as latent print exposure increases. Implications for consideration of unipolar IRT with other constructs and measures (e.g., vocabulary, specialized forms of knowledge) that reflect a similar type of response process are considered in the discussion., (© 2023. The Psychonomic Society, Inc.)

A testlet-based visual analogue scale (VAS) is a doubly bounded scaling approach (e.g., from 0% to 100% or from 0 to 1) composed of multiple adjectives, nouns, or sentences (statements/items) within testlets for measuring individuals' attitudes, opinions, or career interests. While testlet-based VASs have many advantages over Likert scales, such as reducing response style effects, the development of proper statistical models for analyzing testlet-based VAS data lags behind. This paper proposes a novel beta copula model and a competing logit-normal model based on the item response theory framework, assessed by Bayesian parameter estimation, model comparison, and goodness-of-fit statistics. An empirical career interest dataset based on a testlet-based VAS design was analyzed using the proposed models. Simulation studies were conducted to assess the two models' parameter recovery. The results show that the beta copula model had superior fit in the empirical data analysis, and also exhibited good parameter recovery in the simulation studies, suggesting that it is a promising statistical approach to testlet-based doubly bounded responses., (© 2023. The Psychonomic Society, Inc.)

The present work intends to discuss parameter estimation and statistical analysis in adsorption. The Langmuir and Tóth isotherm models are compared for a set of carbon dioxide adsorption data on 13X zeolite from literature at different temperatures: 303, 323, 373, and 423 K. Statistical analyses were performed under frequentist and Bayesian perspectives. Under the frequentist statistical view, parameters were estimated using Maximum Likelihood estimation (MLE). Statistical analyses of parameters were performed by confidence regions in terms of elliptical approximation and likelihood region, while the evaluation of models was performed by chi-square statistics. The results showed that, for these nonlinear models, the elliptical region offers a poor approximation of the parameter estimates' confidence region, especially for the most correlated parameter pairs. Additionally, the four-parameter Tóth's equation yields less correlated parameters than the three-parameter Langmuir model. From a Bayesian perspective, the Markov chain Monte Carlo (MCMC) technique facilitated the reconstruction of the probability density functions of parameters as well as enabled the propagation of parametric uncertainties in the model responses. Finally, the accurate assessment of experimental uncertainty significantly influences the evaluation of models and their respective parameters., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Conditional process models, including moderated mediation models and mediated moderation models, are widely used in behavioral science research. However, few studies have examined approaches to conduct statistical power analysis for such models and there is also a lack of software packages that provide such power analysis functionalities. In this paper, we introduce new simulation-based methods for power analysis of conditional process models with a focus on moderated mediation models. These simulation-based methods provide intuitive ways for sample-size planning based on regression coefficients in a moderated mediation model as well as selected variance and covariance components. We demonstrate how the methods can be applied to five commonly used moderated mediation models using a simulation study, and we also assess the performance of the methods through the five models. We implement our approaches in the WebPower R package and also in Web apps to ease their application., (© 2024. The Psychonomic Society, Inc.)

State-of-the-art participant-specific finite element models require advanced medical imaging to quantify bone geometry and density distribution; access to and cost of imaging is prohibitive to the use of this approach. Statistical appearance models may enable estimation of participants' geometry and density in the absence of medical imaging. The purpose of this study was to: (1) quantify errors associated with predicting tibia-fibula geometry and density distribution from skin-mounted landmarks using a statistical appearance model and (2) quantify how those errors propagate to finite element-calculated bone strain. Participant-informed models of the tibia and fibula were generated for thirty participants from height and sex and from twelve skin-mounted landmarks using a statistical appearance model. Participant-specific running loads, calculated using gait data and a musculoskeletal model, were applied to participant-informed and CT-based models to predict bone strain using the finite element method. Participant-informed meshes illustrated median geometry and density distribution errors of 4.39-5.17 mm and 0.116-0.142 g/cm3, respectively, resulting in large errors in strain distribution (median RMSE = 476-492 με), peak strain (limits of agreement =±27-34%), and strained volume (limits of agreement =±104-202%). These findings indicate that neither skin-mounted landmark nor height and sex-based predictions could adequately approximate CT-derived participant-specific geometry, density distribution, or finite element-predicted bone strain and therefore should not be used for analyses comparing between groups or individuals., (Copyright © 2024 by ASME.)

In epidemiology, realistic disease dynamics often require Susceptible-Exposed-Infected-Recovered (SEIR)-like models because they account for incubation periods before individuals become infectious. However, for the sake of analytical tractability, simpler Susceptible-Infected-Recovered (SIR) models are commonly used, despite their lack of biological realism. Bridging these models is crucial for accurately estimating parameters and fitting models to observed data, particularly in population-level studies of infectious diseases. This paper investigates stochastic versions of the SEIR and SIR frameworks and demonstrates that the SEIR model can be effectively approximated by a SIR model with time-dependent infection and recovery rates. The validity of this approximation is supported by the derivation of a large-population Functional Law of Large Numbers (FLLN) limit and a finite-population concentration inequality. To apply this approximation in practice, the paper introduces a parameter inference methodology based on the Dynamic Survival Analysis (DSA) survival analysis framework. This method enables the fitting of the SIR model to data simulated from the more complex SEIR dynamics, as illustrated through simulated experiments., Competing Interests: Declaration of competing interest The authors declare no conflicts of interest., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Objective: Papillary thyroid carcinoma (PTC) is a common malignancy whose incidence is three times greater in females than in males. The prognosis of ageing patients is poor. This research was designed to construct models to predict the overall survival of elderly female patients with PTC., Methods: We developed prediction models based on the random survival forest (RSF) algorithm and traditional Cox regression. The data of 4539 patients were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Twelve variables were analysed to establish the models. The C-index and the Brier score were selected to evaluate the discriminatory ability of the models. Time-dependent receiver operating characteristic (ROC) curves were also drawn to evaluate the accuracy of the models. The clinical benefits of the two models were compared on the basis of the DCA curve. In addition, the Shapley Additive Explanations (SHAP) plot was used to visualize the contribution of the variables in the RSF model., Results: The C-index of the RSF model was 0.811, which was greater than that of the Cox model (0.781). According to the Brier score and the area under the ROC curve (AUC), the RSF model performed better than the Cox model. On the basis of the DCA curve, the RSF model demonstrated fair clinical benefit. The SHAP plot showed that age was the most important variable contributing to the outcome of PTC in elderly female patients., Conclusions: The RSF model we developed performed better than the Cox model and might be valuable for clinical practice., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Computational modelling was used to assess the capability of a deterministic and a probabilistic method to predict the incidence of AIS3+ injuries in passenger car occupants by comparing the predictions of the methods to the actual injuries observed in real-world crashes. The likelihood of sustaining an injury was first calculated using a computer model for a selected set of injury criteria in different impact conditions based on real-world crashes; AIS3+ injuries were then predicted using each method separately. Regardless of the method, the number of serious injuries was over-predicted. It was also noted that the used injury criteria suggested the occurrence of specific injuries that were not observed in the real world. Although both methods are susceptible to be adapted to improve their predictions, the question of the suitability of using some of the most commonly accepted injury criteria used with crash test dummies for injury assessment with human body models deserves further research.

This study proposes a procedure for substantive dimensionality estimation in the presence of wording effects, the inconsistent response to regular and reversed self-report items. The procedure developed consists of subtracting an approximate estimate of the wording effects variance from the sample correlation matrix and then estimating the substantive dimensionality on the residual correlation matrix. This is achieved by estimating a random intercept factor with unit loadings for all the regular and unrecoded reversed items. The accuracy of the procedure was evaluated through an extensive simulation study that manipulated nine relevant variables and employed the exploratory graph analysis (EGA) and parallel analysis (PA) retention methods. The results indicated that combining the proposed procedure with EGA or PA achieved high accuracy in estimating the substantive latent dimensionality, but that EGA was superior. Additionally, the present findings shed light on the complex ways that wording effects impact the dimensionality estimates when the response bias in the data is ignored. A tutorial on substantive dimensionality estimation with the R package EGAnet is offered, as well as practical guidelines for applied researchers., (© 2024. The Psychonomic Society, Inc.)

Random survival forests (RSF) can be applied to many time-to-event research questions and are particularly useful in situations where the relationship between the independent variables and the event of interest is rather complex. However, in many clinical settings, the occurrence of the event of interest is affected by competing events, which means that a patient can experience an outcome other than the event of interest. Neglecting the competing event (i.e., regarding competing events as censoring) will typically result in biased estimates of the cumulative incidence function (CIF). A popular approach for competing events is Fine and Gray's subdistribution hazard model, which directly estimates the CIF by fitting a single-event model defined on a subdistribution timescale. Here, we integrate concepts from the subdistribution hazard modeling approach into the RSF. We develop several imputation strategies that use weights as in a discrete-time subdistribution hazard model to impute censoring times in cases where a competing event is observed. Our simulations show that the CIF is well estimated if the imputation already takes place outside the forest on the overall dataset. Especially in settings with a low rate of the event of interest or a high censoring rate, competing events must not be neglected, that is, treated as censoring. When applied to a real-world epidemiological dataset on chronic kidney disease, the imputation approach resulted in highly plausible predictor-response relationships and CIF estimates of renal events., (© 2024 The Author(s). Biometrical Journal published by Wiley‐VCH GmbH.)

In health and clinical research, medical indices (eg, BMI) are commonly used for monitoring and/or predicting health outcomes of interest. While single-index modeling can be used to construct such indices, methods to use single-index models for analyzing longitudinal data with multiple correlated binary responses are underdeveloped, although there are abundant applications with such data (eg, prediction of multiple medical conditions based on longitudinally observed disease risk factors). This article aims to fill the gap by proposing a generalized single-index model that can incorporate multiple single indices and mixed effects for describing observed longitudinal data of multiple binary responses. Compared to the existing methods focusing on constructing marginal models for each response, the proposed method can make use of the correlation information in the observed data about different responses when estimating different single indices for predicting response variables. Estimation of the proposed model is achieved by using a local linear kernel smoothing procedure, together with methods designed specifically for estimating single-index models and traditional methods for estimating generalized linear mixed models. Numerical studies show that the proposed method is effective in various cases considered. It is also demonstrated using a dataset from the English Longitudinal Study of Aging project., (© 2024 John Wiley & Sons Ltd.)

The Cox regression model or accelerated failure time regression models are often used for describing the relationship between survival outcomes and potential explanatory variables. These models assume the studied covariates are connected to the survival time or its distribution or their transformations through a function of a linear regression form. In this article, we propose nonparametric, nonlinear algorithms (deepAFT methods) based on deep artificial neural networks to model survival outcome data in the broad distribution family of accelerated failure time models. The proposed methods predict survival outcomes directly and tackle the problem of censoring via an imputation algorithm as well as re-weighting and transformation techniques based on the inverse probabilities of censoring. Through extensive simulation studies, we confirm that the proposed deepAFT methods achieve accurate predictions. They outperform the existing regression models in prediction accuracy, while being flexible and robust in modeling covariate effects of various nonlinear forms. Their prediction performance is comparable to other established deep learning methods such as deepSurv and random survival forest methods. Even though the direct output is the expected survival time, the proposed AFT methods also provide predictions for distributional functions such as the cumulative hazard and survival functions without additional learning efforts. For situations where the popular Cox regression model may not be appropriate, the deepAFT methods provide useful and effective alternatives, as shown in simulations, and demonstrated in applications to a lymphoma clinical trial study., (© 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.)

An assurance calculation is a Bayesian alternative to a power calculation. One may be performed to aid the planning of a clinical trial, specifically setting the sample size or to support decisions about whether or not to perform a study. Immuno-oncology is a rapidly evolving area in the development of anticancer drugs. A common phenomenon that arises in trials of such drugs is one of delayed treatment effects, that is, there is a delay in the separation of the survival curves. To calculate assurance for a trial in which a delayed treatment effect is likely to be present, uncertainty about key parameters needs to be considered. If uncertainty is not considered, the number of patients recruited may not be enough to ensure we have adequate statistical power to detect a clinically relevant treatment effect and the risk of an unsuccessful trial is increased. We present a new elicitation technique for when a delayed treatment effect is likely and show how to compute assurance using these elicited prior distributions. We provide an example to illustrate how this can be used in practice and develop open-source software to implement our methods. Our methodology has the potential to improve the success rate and efficiency of Phase III trials in immuno-oncology and for other treatments where a delayed treatment effect is expected to occur., (© 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.)

We consider the Bayesian estimation of the parameters of a finite mixture model from independent order statistics arising from imperfect ranked set sampling designs. As a cost-effective method, ranked set sampling enables us to incorporate easily attainable characteristics, as ranking information, into data collection and Bayesian estimation. To handle the special structure of the ranked set samples, we develop a Bayesian estimation approach exploiting the Expectation-Maximization (EM) algorithm in estimating the ranking parameters and Metropolis within Gibbs Sampling to estimate the parameters of the underlying mixture model. Our findings show that the proposed RSS-based Bayesian estimation method outperforms the commonly used Bayesian counterpart using simple random sampling. The developed method is finally applied to estimate the bone disorder status of women aged 50 and older., (© 2024 John Wiley & Sons Ltd.)

There is growing interest in platform trials that allow for adding of new treatment arms as the trial progresses as well as being able to stop treatments part way through the trial for either lack of benefit/futility or for superiority. In some situations, platform trials need to guarantee that error rates are controlled. This paper presents a multi-stage design, that allows additional arms to be added in a platform trial in a preplanned fashion, while still controlling the family-wise error rate, under the assumption of known number and timing of treatments to be added, and no time trends. A method is given to compute the sample size required to achieve a desired level of power and we show how the distribution of the sample size and the expected sample size can be found. We focus on power under the least favorable configuration which is the power of finding the treatment with a clinically relevant effect out of a set of treatments while the rest have an uninteresting treatment effect. A motivating trial is presented which focuses on two settings, with the first being a set number of stages per active treatment arm and the second being a set total number of stages, with treatments that are added later getting fewer stages. Compared to Bonferroni, the savings in the total maximum sample size are modest in a trial with three arms, <1% of the total sample size. However, the savings are more substantial in trials with more arms., (© 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.)

Biomarkers are often measured in bulk to diagnose patients, monitor patient conditions, and research novel drug pathways. The measurement of these biomarkers often suffers from detection limits that result in missing and untrustworthy measurements. Frequently, missing biomarkers are imputed so that down-stream analysis can be conducted with modern statistical methods that cannot normally handle data subject to informative censoring. This work develops an empirical Bayes g $$ g $$ -modeling method for imputing and denoising biomarker measurements. We establish superior estimation properties compared to popular methods in simulations and with real data, providing the useful biomarker measurement estimations for down-stream analysis., (© 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.)

Emergency medical diseases (EMDs) are the leading cause of death worldwide. A time-to-death analysis is needed to accurately identify the risks and describe the pattern of an EMD because the mortality rate can peak early and then decline. Dose-ranging Phase II clinical trials are essential for developing new therapies for EMDs. However, most dose-finding trials do not analyze mortality as a time-to-event endpoint. We propose three Bayesian dose-response time-to-event models for a secondary mortality analysis of a clinical trial: a two-group (active treatment vs control) model, a three-parameter sigmoid EMAX model, and a hierarchical EMAX model. The study also incorporates one specific active treatment as an active comparator in constructing three new models. We evaluated the performance of these six models and a very popular independent model using simulated data motivated by a randomized Phase II clinical trial focused on identifying the most effective hyperbaric oxygen dose to achieve favorable functional outcomes in patients with severe traumatic brain injury. The results show that the three-group, EMAX, and EMAX model with an active comparator produce the smallest averaged mean squared errors and smallest mean absolute biases. We provide a new approach for time-to-event analysis in early-phase dose-ranging clinical trials for EMDs. The EMAX model with an active comparator can provide valuable insights into the mortality analysis of new EMDs or other conditions that have changing risks over time. The restricted mean survival time, a function of the model's hazards, is recommended for displaying treatment effects for EMD research., (© 2024 John Wiley & Sons Ltd.)

Recent advances in engineering technologies have enabled the collection of a large number of longitudinal features. This wealth of information presents unique opportunities for researchers to investigate the complex nature of diseases and uncover underlying disease mechanisms. However, analyzing such kind of data can be difficult due to its high dimensionality, heterogeneity and computational challenges. In this article, we propose a Bayesian nonparametric mixture model for clustering high-dimensional mixed-type (eg, continuous, discrete and categorical) longitudinal features. We employ a sparse factor model on the joint distribution of random effects and the key idea is to induce clustering at the latent factor level instead of the original data to escape the curse of dimensionality. The number of clusters is estimated through a Dirichlet process prior. An efficient Gibbs sampler is developed to estimate the posterior distribution of the model parameters. Analysis of real and simulated data is presented and discussed. Our study demonstrates that the proposed model serves as a useful analytical tool for clustering high-dimensional longitudinal data., (© 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.)

An important strategy for identifying principal causal effects (popular estimands in settings with noncompliance) is to invoke the principal ignorability (PI) assumption. As PI is untestable, it is important to gauge how sensitive effect estimates are to its violation. We focus on this task for the common one-sided noncompliance setting where there are two principal strata, compliers and noncompliers. Under PI, compliers and noncompliers share the same outcome-mean-given-covariates function under the control condition. For sensitivity analysis, we allow this function to differ between compliers and noncompliers in several ways, indexed by an odds ratio, a generalized odds ratio, a mean ratio, or a standardized mean difference sensitivity parameter. We tailor sensitivity analysis techniques (with any sensitivity parameter choice) to several types of PI-based main analysis methods, including outcome regression, influence function (IF) based and weighting methods. We discuss range selection for the sensitivity parameter. We illustrate the sensitivity analyses with several outcome types from the JOBS II study. This application estimates nuisance functions parametrically - for simplicity and accessibility. In addition, we establish rate conditions on nonparametric nuisance estimation for IF-based estimators to be asymptotically normal - with a view to inform nonparametric inference., (© 2024 John Wiley & Sons Ltd.)

Policymakers often require information on programs' long-term impacts that is not available when decisions are made. For example, while rigorous evidence from the Oregon Health Insurance Experiment (OHIE) shows that having health insurance influences short-term health and financial measures, the impact on long-term outcomes, such as mortality, will not be known for many years following the program's implementation. We demonstrate how data fusion methods may be used address the problem of missing final outcomes and predict long-run impacts of interventions before the requisite data are available. We implement this method by concatenating data on an intervention (such as the OHIE) with auxiliary long-term data and then imputing missing long-term outcomes using short-term surrogate outcomes while approximating uncertainty with replication methods. We use simulations to examine the performance of the methodology and apply the method in a case study. Specifically, we fuse data on the OHIE with data from the National Longitudinal Mortality Study and estimate that being eligible to apply for subsidized health insurance will lead to a statistically significant improvement in long-term mortality., (© 2024 John Wiley & Sons Ltd.)

Background: Distributed statistical analyses provide a promising approach for privacy protection when analyzing data distributed over several databases. Instead of directly operating on data, the analyst receives anonymous summary statistics, which are combined into an aggregated result. Further, in discrimination model (prognosis, diagnosis, etc.) development, it is key to evaluate a trained model w.r.t. to its prognostic or predictive performance on new independent data. For binary classification, quantifying discrimination uses the receiver operating characteristics (ROC) and its area under the curve (AUC) as aggregation measure. We are interested to calculate both as well as basic indicators of calibration-in-the-large for a binary classification task using a distributed and privacy-preserving approach., Methods: We employ DataSHIELD as the technology to carry out distributed analyses, and we use a newly developed algorithm to validate the prediction score by conducting distributed and privacy-preserving ROC analysis. Calibration curves are constructed from mean values over sites. The determination of ROC and its AUC is based on a generalized linear model (GLM) approximation of the true ROC curve, the ROC-GLM, as well as on ideas of differential privacy (DP). DP adds noise (quantified by the ℓ 2 sensitivity Δ 2 ( f ^ ) ) to the data and enables a global handling of placement numbers. The impact of DP parameters was studied by simulations., Results: In our simulation scenario, the true and distributed AUC measures differ by Δ AUC < 0.01 depending heavily on the choice of the differential privacy parameters. It is recommended to check the accuracy of the distributed AUC estimator in specific simulation scenarios along with a reasonable choice of DP parameters. Here, the accuracy of the distributed AUC estimator may be impaired by too much artificial noise added from DP., Conclusions: The applicability of our algorithms depends on the ℓ 2 sensitivity Δ 2 ( f ^ ) of the underlying statistical/predictive model. The simulations carried out have shown that the approximation error is acceptable for the majority of simulated cases. For models with high Δ 2 ( f ^ ) , the privacy parameters must be set accordingly higher to ensure sufficient privacy protection, which affects the approximation error. This work shows that complex measures, as the AUC, are applicable for validation in distributed setups while preserving an individual's privacy., (© 2024. The Author(s).)

An N-of-1 observational design characterizes associations among several variables over time in a single individual. Traditional statistical models recommended for experimental N-of-1 trials may not adequately model these observational relationships. We propose an additive Bayesian network using a generalized linear mixed-effects model for the local mean as a novel method for modeling each of these relationships in a data-driven manner. We validate our approach via simulation studies and apply it to a 12-month observational N-of-1 study exploring the impact of stress on daily exercise engagement. We demonstrate the improved performance of the additive Bayesian network to recover the underlying network structure. From the empirical study, we found statistically discernible associations between reports of stress and physical activity on a population level, but these associations may differ at an individual level., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Pascual et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Introduction: While there is an interest in defining longitudinal change in people with chronic illness like Parkinson's disease (PD), statistical analysis of longitudinal data is not straightforward for clinical researchers. Here, we aim to demonstrate how the choice of statistical method may influence research outcomes, (e.g., progression in apathy), specifically the size of longitudinal effect estimates, in a cohort., Methods: In this retrospective longitudinal analysis of 802 people with typical Parkinson's disease in the Luxembourg Parkinson's study, we compared the mean apathy scores at visit 1 and visit 8 by means of the paired two-sided t-test. Additionally, we analysed the relationship between the visit numbers and the apathy score using linear regression and longitudinal two-level mixed effects models., Results: Mixed effects models were the only method able to detect progression of apathy over time. While the effects estimated for the group comparison and the linear regression were smaller with high p-values (+ 1.016/ 7 years, p = 0.107, -0.056/ 7 years, p = 0.897, respectively), effect estimates for the mixed effects models were positive with a very small p-value, indicating a significant increase in apathy symptoms by + 2.345/ 7 years (p < 0.001)., Conclusion: The inappropriate use of paired t-tests and linear regression to analyse longitudinal data can lead to underpowered analyses and an underestimation of longitudinal change. While mixed effects models are not without limitations and need to be altered to model the time sequence between the exposure and the outcome, they are worth considering for longitudinal data analyses. In case this is not possible, limitations of the analytical approach need to be discussed and taken into account in the interpretation., (© 2024. The Author(s).)

We developed and validated a statistical prediction model using 2.5 electronic health records from 24 German emergency departments (EDs) to estimate treatment timeliness at triage. The model's moderate fit and reliance on interoperable, routine data suggest its potential for implementation in ED crowding management.

To address privacy and ethical issues in using health data for machine learning, we evaluate the scalability of advanced synthetic data generation methods like GANs, VAEs, copulaGAN, and transformer models specifically for patient service utilization data. Our study examines five models on data from a Canadian health authority, focusing on training and generation efficiency, data resemblance, and practical utility. Our findings indicate that statistical models excel in efficiency, while most models produce synthetic data that closely mirrors real data, and is also useful for real-world applications.

This study investigates the forecasting of cardiovascular mortality trends in Greece's elderly population. Utilizing mortality data from 2001 to 2020, we employ two forecasting models: the Autoregressive Integrated Moving Average (ARIMA) and Facebook's Prophet model. Our study evaluates the efficacy of these models in predicting cardiovascular mortality trends over 2020-2030. The ARIMA model showcased predictive accuracy for the general and male population within the 65-79 age group, whereas the Prophet model provided better forecasts for females in the same age bracket. Our findings emphasize the need for adaptive forecasting tools that accommodate demographic-specific characteristics and highlight the role of advanced statistical methods in health policy planning.

Recently, it was recommended to omit tied observations before applying the two-sample Wilcoxon-Mann-Whitney test McGee M. et al. (2018). Using a simulation study, we argue for exact tests using all the data (including tied values) as a preferable approach. Exact tests, with tied observations included guarantee the type I error rate with a better exploitation of the significance level and a larger power than the corresponding tests after the omission of tied observations. The omission of ties can produce a considerable change in the shape of the sample, and so can violate underlying test assumptions. Thus, on both theoretical and practical grounds, the recommendation to omit tied values cannot be supported, relative to analysing the whole data set in the same way whether or not ties occur, preferably with an exact permutation test., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Neuhäuser, Ruxton. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

This research explores the application of quadratic polynomials in Python for advanced data analysis. The study demonstrates how quadratic models can effectively capture nonlinear relationships in complex datasets by leveraging Python libraries such as NumPy, Matplotlib, scikit-learn, and Pandas. The methodology involves fitting quadratic polynomials to the data using least-squares regression and evaluating the model fit using the coefficient of determination (R-squared). The results highlight the strong performance of the quadratic polynomial fit, as evidenced by high R-squared values, indicating the model's ability to explain a substantial proportion of the data variability. Comparisons with linear and cubic models further underscore the quadratic model's balance between simplicity and precision for many practical applications. The study also acknowledges the limitations of quadratic polynomials and proposes future research directions to enhance their accuracy and efficiency for diverse data analysis tasks. This research bridges the gap between theoretical concepts and practical implementation, providing an accessible Python-based tool for leveraging quadratic polynomials in data analysis., Competing Interests: Competing interests: Dr. Sipakov is affiliated with CoastalQuant, Inc., which has funded this research. Although the opinions expressed in this paper are those of the authors, they may be influenced by the interests of CoastalQuant, Inc., its clients, affiliates, or employees., (Copyright: © 2024 Sipakov R et al.)

An arrayed CRISPR screen is a high-throughput functional genomic screening method, which typically uses 384 well plates and has different gene knockouts in different wells. Despite various computational workflows, there is currently no systematic way to find what is a good workflow for arrayed CRISPR screening data analysis. To guide this choice, we developed a statistical simulation model that mimics the data generating process of arrayed CRISPR screening experiments. Our model is flexible and can simulate effects on phenotypic readouts of various experimental factors, such as the effect size of gene editing, as well as biological and technical variations. With two examples, we showed that the simulation model can assist making principled choice of normalization and hit calling method for the arrayed CRISPR data analysis. This simulation model is implemented in an R package and can be downloaded from Github., Competing Interests: All authors are employees of AstraZeneca and has stock ownership and/or stock options or interests in the company. This does not alter our adherence to PLOS ONE policies on sharing data and materials., (Copyright: © 2024 Kim et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: The COVID-19 pandemic has profoundly impacted all aspects of human life for over 3 years. Understanding the evolution of public risk perception during these periods is crucial. Few studies explore the mechanisms for reducing disease transmission due to risk perception. Thus, we hypothesize that changes in human mobility play a mediating role between risk perception and the progression of the pandemic., Objective: The study aims to explore how various forms of human mobility, including essential, nonessential, and job-related behaviors, mediate the temporal relationships between risk perception and pandemic dynamics., Methods: We used distributed-lag linear structural equation models to compare the mediating impact of human mobility across different virus variant periods. These models examined the temporal dynamics and time-lagged effects among risk perception, changes in mobility, and virus transmission in Taiwan, focusing on two distinct periods: (1) April-August 2021 (pre-Omicron era) and (2) February-September 2022 (Omicron era)., Results: In the pre-Omicron era, our findings showed that an increase in public risk perception correlated with significant reductions in COVID-19 cases across various types of mobility within specific time frames. Specifically, we observed a decrease of 5.59 (95% CI -4.35 to -6.83) COVID-19 cases per million individuals after 7 weeks in nonessential mobility, while essential mobility demonstrated a reduction of 10.73 (95% CI -9.6030 to -11.8615) cases after 8 weeks. Additionally, job-related mobility resulted in a decrease of 3.96 (95% CI -3.5039 to -4.4254) cases after 11 weeks. However, during the Omicron era, these effects notably diminished. A reduction of 0.85 (95% CI -1.0046 to -0.6953) cases through nonessential mobility after 10 weeks and a decrease of 0.69 (95% CI -0.7827 to -0.6054) cases through essential mobility after 12 weeks were observed., Conclusions: This study confirms that changes in mobility serve as a mediating factor between heightened risk perception and pandemic mitigation in both pre-Omicron and Omicron periods. This suggests that elevating risk perception is notably effective in impeding virus progression, especially when vaccines are unavailable or their coverage remains limited. Our findings provide significant value for health authorities in devising policies to address the global threats posed by emerging infectious diseases., (© Min-Chien Chang, Tzai-Hung Wen. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org).)

Objectives: The impact of the systematic screening of Neisseria gonorrhoeae (NG) and Chlamydia trachomatis (CT) in men having sex with men (MSM) on these pathogens' epidemiology remains unclear. We conducted a modelling study to analyse this impact in French MSM., Methods: We modelled NG and CT transmission using a site-specific deterministic compartmental model. We calibrated NG and CT prevalence at baseline using results from MSM enrolled in the Dat'AIDS cohort. The baseline scenario was based on 1 million MSM, 40 000 of whom were tested every 90 days and 960 000 every 200 days. Incidence rate ratios (IRRs) at steady state were simulated for NG, CT, NG and/or CT infections, for different combinations of tested sites, testing frequency and numbers of frequently tested patients., Results: The observed prevalence rate was 11.0%, 10.5% and 19.1% for NG, CT and NG and/or CT infections. The baseline incidence rate was estimated at 138.2 per year per 100 individuals (/100PY), 86.8/100PY and 225.0/100PY for NG, CT and NG and/or CT infections. Systematically testing anal, pharyngeal and urethral sites at the same time reduced incidence by 14%, 23% and 18% (IRR: 0.86, 0.77 and 0.82) for NG, CT and NG and/or CT infections. Reducing the screening interval to 60 days in frequently tested patients reduced incidence by 20%, 29% and 24% (IRR: 0.80, 0.71 and 0.76) for NG, CT and NG and/or CT infections. Increasing the number of frequently tested patients to 200 000 reduced incidence by 29%, 40% and 33% (IRR: 0.71, 0.60 and 0.67) for NG, CT and NG and/or CT infections. No realistic scenario could decrease pathogens' incidence by more than 50%., Conclusions: To curb the epidemic of NG and CT in MSM, it would not only be necessary to drastically increase screening, but also to add other combined interventions., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Rifampicin resistant tuberculosis (RR-TB) poses a growing threat to individuals and communities. This study utilized a seasonal autoregressive integrated moving average (SARIMA) model to quantitatively predict the monthly incidence of RR-TB in Yunnan Province which could guide government health administration departments and the centers for disease control and prevention (CDC) in preventing and controlling the RR-TB epidemic., Methods: The study utilized routine surveillance reporting data from the infectious Disease Network Surveillance and Reporting System. Monthly incidence rates of RR-TB were collected from January 2019 to December 2022. A time series SARIMA model was used to predict the number of monthly RR-TB cases in Yunnan Province in 2023, and the model was validated using time series plots, seasonal and non-seasonal differencing, autocorrelation and partial autocorrelation analysis, and white noise tests., Results: From 2019 to 2022, the incidence of RR-TB decreases as the incidence of all TB decreases (P < 0.05). There was no significant change in the proportion of RR-TB among all TB cases, which remained within 2.5% (P>0.05). The time series decomposition shows that it presented obvious seasonality, periodicity and randomness after being decomposed. Time series analysis was performed on the original series after 1 non-seasonal difference and 1 seasonal difference, the ADF test showed P < 0.05. According to ACF and PACF, the SARIMA (1, 1, 1) (1, 1, 0) 12 model was chosen and statistically significant model parameter estimates (P < 0.05). The predicted seasonal trend of RR-TB incidence in 2019 to 2023 was similar to the actual data. The percentage accuracy in the prediction excesses 80% in 2019 to 2022 and is all within 95% CI. However there was a certain gap between the actual incidence and the predicted value in 2023, and the acutual incidence had increased by 12.4% compared to 2022. The percentage of accuracy in the prediction was only 70% in 2023., Conclusions: We found the incidence of RR-TB was based on that of all TB in Yunnan. The SARIMA model successfully predicted the seasonal incidence trend of RR-TB in Yunnan Province in 2019 to 2023, but the prediction precision could be influenced by factors such as new infectious disease outbreaks or pandemics, social issues, environmental challenges or other unknown risks. Hence CDCs should pay special attention to the post epidemic effects of new infectious disease outbreaks or pandemics, carry out monitoring and early warning, and better optimize disease prediction models., (© 2024. The Author(s).)

The study was conducted in order to study breakfast skipping (BKS) frequency, factors associated with, health consequence and undergraduate students academic performance during Covid-19 pandemic as earliest studies focusing on this area. A cross-sectional study was carried out among 2225 of undergraduate students. The study was carried between the period of 15/1/2020 to 3/4/2020 using an online self-report Breakfast Eating Habit Survey (BEHS). The BEHS survey was divided into two sections. The first sections included sociodemographic information (gender, BMI, age, smoking, residency, parental education, family income, studying system and stage (public or private), and studying institution (university or institute) academic performance. The second part included questions regarding breakfast eating habits including frequency of skipping meals, factors related to BKS health consequences and types of snacks. Logistic regression is a common technique used for modeling outcomes that fall into the range of 1 and 0. For this purpose, a logistic regression was performed to find adjusted odds ratio and crude odds ratio. The results showed that the majority of participants were female (1238, 55.7%). Out of 2,224 students, 2059 are aged between 18 to 24 years. Most of the participants were from first level (26.5%), second level (32.8%), third level (17.6%) or the fourth level (21.3%). Over 92% of participants were single and about 68% came from families of medium income families. The statistical analysis showed that the odds of BKS is reduced among students who live in accommodation by 54% (odds ratio = 54%, CI (41-71%), p value = 0.000). It seems that students with low income and normal or higher BMI are more likely to skip breakfast more regularly. The odds of skipping breakfast among students with BMI of 18-24.9 is reduced by 41% (odds ratio = 59%, CI (27%-93%), p value = 0.027) and the odds of BKS is reduced among students with BMI of 25-29.9 by 45% (odds ratio = 55%, CI (31-95%). Additionally, students with medium or high incomes are more likely to skip breakfast as much as twofold in comparison with students with low income (medium income (odds ratio = 1.85, CI (1.08-3.17), p-value = 0.024), high income (odds ratio = 1.98, CI (1.12-3.51), p-value = 0.019). The most common reasons for skipping breakfast included include time constraint, not hungry, breakfast is not ready, afraid to be overweight and lack of appetite. The consequences of skipping breakfast were feeling hungry throughout the day, feeling tired, and not paying attention in class and low academic performance. To concluded, BKS during Covid-19 is more common among students with higher BMI, higher income and living in accommodation. The main reason is time constraint and the most common health problems are being tired and luck of attention., (© 2024. The Author(s).)

In disciplines from biology to climate science, a routine task is to compute a correlation between a pair of time series and determine whether the correlation is statistically significant (i.e., unlikely under the null hypothesis that the time series are independent). This problem is challenging because time series typically exhibit autocorrelation and thus cannot be properly analyzed with the standard iid-oriented statistical tests. Although there are well-known parametric tests for time series, these are designed for linear correlation statistics and thus not suitable for the increasingly popular nonlinear correlation statistics. There are also nonparametric tests that can be used with any correlation statistic, but for these, the conditions that guarantee correct false positive rates are either restrictive or unclear. Here, we describe the truncated time-shift (TTS) test, a nonparametric procedure to test for dependence between 2 time series. We prove that this test correctly controls the false positive rate as long as one of the time series is stationary, a minimally restrictive requirement among current tests. The TTS test is versatile because it can be used with any correlation statistic. Using synthetic data, we demonstrate that this test performs correctly even while other tests suffer high false positive rates. In simulation examples, simple guidelines for parameter choices allow high statistical power to be achieved with sufficient data. We apply the test to datasets from climatology, animal behavior, and microbiome science, verifying previously discovered dependence relationships and detecting additional relationships., Competing Interests: WS is a member of the PLOS Biology Editorial Board., (Copyright: © 2024 Yuan, Shou. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Adaptive randomized clinical trials are of major interest when dealing with a time-to-event outcome in a prolonged observation window. No consensus exists either to define stopping boundaries or to combine p $$ p $$ values or test statistics in the terminal analysis in the case of a frequentist design and sample size adaptation. In a one-sided setting, we compared three frequentist approaches using stopping boundaries relying on α $$ \alpha $$ -spending functions and a Bayesian monitoring setting with boundaries based on the posterior distribution of the log-hazard ratio. All designs comprised a single interim analysis with an efficacy stopping rule and the possibility of sample size adaptation at this interim step. Three frequentist approaches were defined based on the terminal analysis: combination of stagewise statistics (Wassmer) or of p $$ p $$ values (Desseaux), or on patientwise splitting (Jörgens), and we compared the results with those of the Bayesian monitoring approach (Freedman). These different approaches were evaluated in a simulation study and then illustrated on a real dataset from a randomized clinical trial conducted in elderly patients with chronic lymphocytic leukemia. All approaches controlled for the type I error rate, except for the Bayesian monitoring approach, and yielded satisfactory power. It appears that the frequentist approaches are the best in underpowered trials. The power of all the approaches was affected by the violation of the proportional hazards (PH) assumption. For adaptive designs with a survival endpoint and a one-sided alternative hypothesis, the Wassmer and Jörgens approaches after sample size adaptation should be preferred, unless violation of PH is suspected., (© 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.)

Moderate calibration, the expected event probability among observations with predicted probability z being equal to z, is a desired property of risk prediction models. Current graphical and numerical techniques for evaluating moderate calibration of risk prediction models are mostly based on smoothing or grouping the data. As well, there is no widely accepted inferential method for the null hypothesis that a model is moderately calibrated. In this work, we discuss recently-developed, and propose novel, methods for the assessment of moderate calibration for binary responses. The methods are based on the limiting distributions of functions of standardized partial sums of prediction errors converging to the corresponding laws of Brownian motion. The novel method relies on well-known properties of the Brownian bridge which enables joint inference on mean and moderate calibration, leading to a unified "bridge" test for detecting miscalibration. Simulation studies indicate that the bridge test is more powerful, often substantially, than the alternative test. As a case study we consider a prediction model for short-term mortality after a heart attack, where we provide suggestions on graphical presentation and the interpretation of results. Moderate calibration can be assessed without requiring arbitrary grouping of data or using methods that require tuning of parameters., (© 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.)

Analysis of competing risks data has been an important topic in survival analysis due to the need to account for the dependence among the competing events. Also, event times are often recorded on discrete time scales, rendering the models tailored for discrete-time nature useful in the practice of survival analysis. In this work, we focus on regression analysis with discrete-time competing risks data, and consider the errors-in-variables issue where the covariates are prone to measurement errors. Viewing the true covariate value as a parameter, we develop the conditional score methods for various discrete-time competing risks models, including the cause-specific and subdistribution hazards models that have been popular in competing risks data analysis. The proposed estimators can be implemented by efficient computation algorithms, and the associated large sample theories can be simply obtained. Simulation results show satisfactory finite sample performances, and the application with the competing risks data from the scleroderma lung study reveals the utility of the proposed methods., (© 2024 John Wiley & Sons Ltd.)

The rise of cutting-edge precision cancer treatments has led to a growing significance of the optimal biological dose (OBD) in modern oncology trials. These trials now prioritize the consideration of both toxicity and efficacy simultaneously when determining the most desirable dosage for treatment. Traditional approaches in early-phase oncology trials have conventionally relied on the assumption of a monotone relationship between treatment efficacy and dosage. However, this assumption may not hold valid for novel oncology therapies. In reality, the dose-efficacy curve of such treatments may reach a plateau at a specific dose, posing challenges for conventional methods in accurately identifying the OBD. Furthermore, achieving reliable identification of the OBD is typically not possible based on a single small-sample trial. With data from multiple phase I and phase I/II trials, we propose a novel Bayesian random-effects dose-optimization meta-analysis (REDOMA) approach to identify the OBD by synthesizing toxicity and efficacy data from each trial. The REDOMA method can address trials with heterogeneous characteristics. We adopt a curve-free approach based on a Gamma process prior to model the average dose-toxicity relationship. In addition, we utilize a Bayesian model selection framework that uses the spike-and-slab prior as an automatic variable selection technique to eliminate monotonic constraints on the dose-efficacy curve. The good performance of the REDOMA method is confirmed by extensive simulation studies., (© 2024 John Wiley & Sons Ltd.)

Ordinal longitudinal outcomes are becoming common in clinical research, particularly in the context of COVID-19 clinical trials. These outcomes are information-rich and can increase the statistical efficiency of a study when analyzed in a principled manner. We present Bayesian ordinal transition models as a flexible modeling framework to analyze ordinal longitudinal outcomes. We develop the theory from first principles and provide an application using data from the Adaptive COVID-19 Treatment Trial (ACTT-1) with code examples in R. We advocate that researchers use ordinal transition models to analyze ordinal longitudinal outcomes when appropriate alongside standard methods such as time-to-event modeling., (© 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.)

Analysis of integrated data often requires record linkage in order to join together the data residing in separate sources. In case linkage errors cannot be avoided, due to the lack a unique identity key that can be used to link the records unequivocally, standard statistical techniques may produce misleading inference if the linked data are treated as if they were true observations. In this paper, we propose methods for categorical data analysis based on linked data that are not prepared by the analyst, such that neither the match-key variables nor the unlinked records are available. The adjustment is based on the proportion of false links in the linked file and our approach allows the probabilities of correct linkage to vary across the records without requiring that one is able to estimate this probability for each individual record. It accommodates also the general situation where unmatched records that cannot possibly be correctly linked exist in all the sources. The proposed methods are studied by simulation and applied to real data., (© 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.)

Multi-arm multi-stage (MAMS) platform trials efficiently compare several treatments with a common control arm. Crucially MAMS designs allow for adjustment for multiplicity if required. If for example, the active treatment arms in a clinical trial relate to different dose levels or different routes of administration of a drug, the strict control of the family-wise error rate (FWER) is paramount. Suppose a further treatment becomes available, it is desirable to add this to the trial already in progress; to access both the practical and statistical benefits of the MAMS design. In any setting where control of the error rate is required, we must add corresponding hypotheses without compromising the validity of the testing procedure.To strongly control the FWER, MAMS designs use pre-planned decision rules that determine the recruitment of the next stage of the trial based on the available data. The addition of a treatment arm presents an unplanned change to the design that we must account for in the testing procedure. We demonstrate the use of the conditional error approach to add hypotheses to any testing procedure that strongly controls the FWER. We use this framework to add treatments to a MAMS trial in progress. Simulations illustrate the possible characteristics of such procedures., (© 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.)

Dysphagia, a common result of other medical conditions, is caused by malfunctions in swallowing physiology resulting in difficulty eating and drinking. The Modified Barium Swallow Study (MBSS), the most commonly used diagnostic tool for evaluating dysphagia, can be assessed using the Modified Barium Swallow Impairment Profile (MBSImP™). The MBSImP assessment tool consists of a hierarchical grouped data structure with multiple domains, a set of components within each domain which characterize specific swallowing physiologies, and a set of tasks scored on a discrete scale within each component. We lack sophisticated approaches to extract patterns of physiologic swallowing impairment from the MBSImP task scores within a component while still recognizing the nested structure of components within a domain. We propose a Bayesian hierarchical profile regression model, which uses a Bayesian profile regression model in conjunction with a hierarchical Dirichlet process mixture model to (1) cluster subjects into impairment profile patterns while respecting the hierarchical grouped data structure of the MBSImP, and (2) simultaneously determine associations between latent profile cluster membership for all components and the outcome of dysphagia severity. We apply our approach to a cohort of patients referred for an MBSS and assessed using the MBSImP. Our research results can be used to inform appropriate intervention strategies, and provide tools for clinicians to make better multidimensional management and treatment decisions for patients with dysphagia., (© 2024 John Wiley & Sons Ltd.)

We investigate the familywise error rate (FWER) for time-to-event endpoints evaluated using a group sequential design with a hierarchical testing procedure for secondary endpoints. We show that, in this setup, the correlation between the log-rank test statistics at interim and at end of study is not congruent with the canonical correlation derived for normal-distributed endpoints. We show, both theoretically and by simulation, that the correlation also depends on the level of censoring, the hazard rates of the endpoints, and the hazard ratio. To optimize operating characteristics in this complex scenario, we propose a simulation-based method to assess the FWER which, better than the alpha-spending approach, can inform the choice of critical values for testing secondary endpoints., (© 2024 John Wiley & Sons Ltd.)

Background: Describing the transmission dynamics of infectious diseases across different regions is crucial for effective disease surveillance. The multivariate time series (MTS) model has been widely adopted for constructing cross-regional infectious disease transmission networks due to its strengths in interpretability and predictive performance. Nevertheless, the assumption of constant parameters frequently disregards the dynamic shifts in disease transmission rates, thereby compromising the accuracy of early warnings. This study investigated the applicability of time-varying MTS models in multi-regional infectious disease monitoring and explored strategies for model selection., Methods: This study focused on two prominent time-varying MTS models: the time-varying parameter-stochastic volatility-vector autoregression (TVP-SV-VAR) model and the time-varying VAR model using the generalized additive framework (tvvarGAM), and intended to explore and verify their applicable conditions for the surveillance of infectious diseases. For the first time, this study proposed the time delay coefficient and spatial sparsity indicators for model selection. These indicators quantify the temporal lags and spatial distribution of infectious disease data, respectively. Simulation study adopted from real-world infectious disease surveillance was carried out to compare model performances under various scenarios of spatio-temporal variation as well as random volatility. Meanwhile, we illustrated how the modelling process could help the surveillance of infectious diseases with an application to the influenza-like case in Sichuan Province, China., Results: When the spatio-temporal variation was small (time delay coefficient: 0.1-0.2, spatial sparsity:0.1-0.3), the TVP-SV-VAR model was superior with smaller fitting residuals and standard errors of parameter estimation than those of the tvvarGAM model. In contrast, the tvvarGAM model was preferable when the spatio-temporal variation increased (time delay coefficient: 0.2-0.3, spatial sparsity: 0.6-0.9)., Conclusion: This study emphasized the importance of considering spatio-temporal variations when selecting appropriate models for infectious disease surveillance. By incorporating our novel indicators-the time delay coefficient and spatial sparsity-into the model selection process, the study could enhance the accuracy and effectiveness of infectious disease monitoring efforts. This approach was not only valuable in the context of this study, but also has broader implications for improving time-varying MTS analyses in various applications., (© 2024. The Author(s).)